CN117238517A - Semi-parametric model GAMLSS-based prediction method for pregnancy females and progestogens - Google Patents
Semi-parametric model GAMLSS-based prediction method for pregnancy females and progestogens Download PDFInfo
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- 230000035935 pregnancy Effects 0.000 title claims abstract description 45
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000000583 progesterone congener Substances 0.000 title claims abstract description 33
- 229940095055 progestogen systemic hormonal contraceptives Drugs 0.000 title description 7
- 238000001514 detection method Methods 0.000 claims abstract description 9
- 238000003018 immunoassay Methods 0.000 claims abstract description 4
- 210000002966 serum Anatomy 0.000 claims abstract description 3
- RJKFOVLPORLFTN-LEKSSAKUSA-N Progesterone Chemical class C1CC2=CC(=O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H](C(=O)C)[C@@]1(C)CC2 RJKFOVLPORLFTN-LEKSSAKUSA-N 0.000 claims description 23
- 239000000262 estrogen Substances 0.000 claims description 20
- 229940011871 estrogen Drugs 0.000 claims description 19
- VOXZDWNPVJITMN-ZBRFXRBCSA-N 17β-estradiol Chemical compound OC1=CC=C2[C@H]3CC[C@](C)([C@H](CC4)O)[C@@H]4[C@@H]3CCC2=C1 VOXZDWNPVJITMN-ZBRFXRBCSA-N 0.000 claims description 6
- 229960005309 estradiol Drugs 0.000 claims description 6
- 229930182833 estradiol Natural products 0.000 claims description 6
- 239000000186 progesterone Substances 0.000 claims description 5
- 229960003387 progesterone Drugs 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 239000013598 vector Substances 0.000 claims description 5
- 238000007476 Maximum Likelihood Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000013179 statistical model Methods 0.000 claims description 3
- 238000011895 specific detection Methods 0.000 claims 1
- 229940088597 hormone Drugs 0.000 description 7
- 239000005556 hormone Substances 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- PROQIPRRNZUXQM-UHFFFAOYSA-N (16alpha,17betaOH)-Estra-1,3,5(10)-triene-3,16,17-triol Natural products OC1=CC=C2C3CCC(C)(C(C(O)C4)O)C4C3CCC2=C1 PROQIPRRNZUXQM-UHFFFAOYSA-N 0.000 description 4
- 229940046836 anti-estrogen Drugs 0.000 description 4
- 230000001833 anti-estrogenic effect Effects 0.000 description 4
- 239000000090 biomarker Substances 0.000 description 4
- PROQIPRRNZUXQM-ZXXIGWHRSA-N estriol Chemical compound OC1=CC=C2[C@H]3CC[C@](C)([C@H]([C@H](O)C4)O)[C@@H]4[C@@H]3CCC2=C1 PROQIPRRNZUXQM-ZXXIGWHRSA-N 0.000 description 4
- 229960001348 estriol Drugs 0.000 description 4
- 239000000328 estrogen antagonist Substances 0.000 description 4
- JWMFYGXQPXQEEM-NUNROCCHSA-N 5β-pregnane Chemical compound C([C@H]1CC2)CCC[C@]1(C)[C@@H]1[C@@H]2[C@@H]2CC[C@H](CC)[C@@]2(C)CC1 JWMFYGXQPXQEEM-NUNROCCHSA-N 0.000 description 3
- 206010028980 Neoplasm Diseases 0.000 description 3
- 230000002028 premature Effects 0.000 description 3
- ORNBQBCIOKFOEO-YQUGOWONSA-N Pregnenolone Natural products O=C(C)[C@@H]1[C@@]2(C)[C@H]([C@H]3[C@@H]([C@]4(C)C(=CC3)C[C@@H](O)CC4)CC2)CC1 ORNBQBCIOKFOEO-YQUGOWONSA-N 0.000 description 2
- 239000000654 additive Substances 0.000 description 2
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- 229960000249 pregnenolone Drugs 0.000 description 2
- ORNBQBCIOKFOEO-QGVNFLHTSA-N pregnenolone Chemical compound C1C=C2C[C@@H](O)CC[C@]2(C)[C@@H]2[C@@H]1[C@@H]1CC[C@H](C(=O)C)[C@@]1(C)CC2 ORNBQBCIOKFOEO-QGVNFLHTSA-N 0.000 description 2
- YWYQTGBBEZQBGO-BERLURQNSA-N Pregnanediol Chemical compound C([C@H]1CC2)[C@H](O)CC[C@]1(C)[C@@H]1[C@@H]2[C@@H]2CC[C@H]([C@@H](O)C)[C@@]2(C)CC1 YWYQTGBBEZQBGO-BERLURQNSA-N 0.000 description 1
- 208000005107 Premature Birth Diseases 0.000 description 1
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- 102000015694 estrogen receptors Human genes 0.000 description 1
- 108010038795 estrogen receptors Proteins 0.000 description 1
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Abstract
The invention discloses a prediction method of pregnancy females and progestins based on a semi-parametric model GAMLSS, which belongs to the technical field of prediction methods of pregnancy females and progestins, and comprises the steps of recruiting clinical pregnant women to be detected, detecting the levels of pregnancy and progestins in serum of the clinical pregnant women by adopting a full-automatic chemiluminescence immunoassay analyzer, sequentially setting average coefficient mu parameters to be identity and log connection according to a detection value data structure, setting model distribution to be Box-Cox normal distribution (BCCG), box-Cox-t distribution (BCT), generalized Beta type 1 distribution (GB 1), gamma distribution (GAMMA) and power exponent distribution (PE), and determining an optimal model according to AIC and BIC minimum values.
Description
Technical Field
The invention relates to a method for predicting pregnancy females and progestogens, in particular to a method for predicting pregnancy females and progestogens based on a semi-parametric model GAMLSS, and belongs to the technical field of methods for predicting pregnancy females and progestogens.
Background
The prior art as disclosed in publication number CN104254779B, an assay for a predictive biomarker with anti-estrogen efficacy, provides a biomarker associated with anti-estrogen sensitivity in cancer, methods for detecting and quantifying the biomarker, and methods for treating cancer patients exhibiting the biomarker. The biomarker is an activated estrogen receptor aggregation site (AEF) found in the nuclei of certain tumor cells. The method provides new information to guide the intent of treating patients with antiestrogens, allowing the selection of individual patients and patient populations likely to respond to treatment.
Methods for screening for AEF inactivation activity of an anti-neoplastic agent or an anti-neoplastic agent candidate are also provided. The method can be used for identifying other AEF active drugs, including antiestrogens, which can be candidates for treating AEF positive tumors according to the method of the invention, and the detection function can be realized in the prior art, but the detection accuracy is not high, so that a prediction method for pregnancy estrogens and progestogens based on a semi-parametric model GAMLSS is designed to solve the problems.
Disclosure of Invention
The invention mainly aims to provide a prediction method of pregnancy estrogen and progestogen based on a semi-parametric model GAMLSS.
The aim of the invention can be achieved by adopting the following technical scheme:
a prediction method of pregnancy female and progestogen based on semi-parameter model GAMLSS,
preferably, the basic probability density function using GAMLSS is f (y i |θ i )；
Wherein:
θ i ＝(μ i ，σ i ，γ i ，τ i )；
μ i as a position parameter, representing a distribution mean;
σ i the scale parameter represents standard deviation;
γ i representing the skewness of the distribution;
τ i is the kurtosis of the distribution.
Preferably, the GAMLSS function is fitted in the form of:
thus, the fitting of four parameters is of general form:
preferably, μ, θ, γ, τ are vectors of length n;
is of length j' k Is a vector of (2);
X k is a fixed n x j' k Is a matrix of (a);
h j4 is the interpretation variable gamma jk Is a smooth non-parametric function of (c);
j＝1,2,3…,J k ，k＝1,2,3,4。
preferably, the GAMLSS method is used for analysis, and after a model is established, screening is performed based on the Akaike Information Criterion and Bayesian information criterion minimum principles.
Preferably, there is an error when the statistical model fits to the data;
using a model to represent a fit may lose some information.
Preferably, akaike Information Criterion is expressed as: akaike Information Criterion =2k—2ln (L);
k is expressed as the number of independent parameters of the model, and L is expressed as a maximum likelihood function of the model.
Preferably, bayesian information criterion is expressed as: bayesian information criterion =ln (n) ×k-2ln (L);
where k is the number of model parameters, n is the sample size, and L is the likelihood function.
6849 pregnant women for clinical labor detection are recruited, and the requirement that hormone medicines are not taken within one week before hormone detection is met; (2) no severe obstetrical disease: including gestational heart disease, gestational diabetes, preeclampsia, and severe liver and kidney diseases. The serum pregnancy and estrogen levels were detected by chemiluminescence immunoassay (Chemiluminescence Analysis, CLIA) using a yabanchect I2000SR full-automatic chemiluminescence immunoassay.
According to the detection value data structure, average coefficient (mu) parameters are sequentially set to be identity and log connection, model distribution is respectively set to be Box-Cox normal distribution (BCCG), box-Cox-t distribution (BCT), generalized Beta type 1 distribution (GB 1), gamma distribution (GAMMA) and power exponent distribution (PE), and an optimal model is determined according to AIC and BIC minimum values.
The beneficial technical effects of the invention are as follows:
the invention provides a prediction method of pregnancy female and gestagen based on semi-parameter model GAMLSS.
Drawings
Fig. 1 is a box plot of a preferred embodiment of a method for predicting pregnancy estrogen and progestin based on a semi-parametric model GAMLSS according to the present invention, wherein the progesterone level increases by about 100-fold, and the estradiol level increases by about 1000-fold during pregnancy, and the pregnancy and progestin change with pregnancy.
Fig. 2 is a graph showing a residual profile of a pregnenolone optimum model fitted by a GAMLSS method according to a preferred embodiment of a half-parameter model GAMLSS method for predicting pregnenolone and progestogen during pregnancy according to the present invention.
Fig. 3 is a graph showing a residual profile of a half-parameter model GAMLSS-based optimal model of pregnane for pregnane-diol fitted by a GAMLSS method according to a preferred embodiment of the method for predicting pregnane for pregnane.
Fig. 4 is a graph showing a residual profile of a gamssy optimized model of estriol during pregnancy fitted by a gamssy method according to a preferred embodiment of a semi-parametric model gamssy for predicting the pregnancy estrogen and progestogen according to the present invention.
Fig. 5 is a graph showing the fit of a semi-parametric model GAMLSS to the increase of pregnancy and estrogen with pregnancy for a preferred embodiment of a method for predicting pregnancy and progestin according to the present invention.
Detailed Description
In order to make the technical solution of the present invention more clear and obvious to those skilled in the art, the present invention will be described in further detail with reference to examples and drawings, but the embodiments of the present invention are not limited thereto.
As shown in fig. 1-5, the present embodiment provides a prediction method for pregnancy females and progestogens based on a semi-parametric model GAMLSS, where the GAMLSS model is a "semi" parametric generalized additive model based on position, proportion and shape, and the parameter distribution defined by the corresponding variables is combined with smooth fitting of the interpretation variable distribution. Unlike the general model, GAMLThe distribution of the corresponding variables in SS is commonly specified by skewness and kurtosis. The basic probability density function of GAMLSS is f (y i |θ i ) Wherein θ i ＝(μ i ，σ i ，γ i ，τ i ). Thus, GAMLSS is determined by four parameters, i.e., μ i As a position parameter, representing a distribution mean; sigma (sigma) i The scale parameter represents standard deviation; gamma ray i Representing the skewness of the distribution, τ i Is the kurtosis of the distribution. Thus, the GAMLSS can model not only the average linearly, but also other parameters to fit additional or random effects.
The fit of the GAMLSS function is generally of the form:
thus, the fitting of four parameters is of general form:
mu, theta, gamma and tau are vectors of length n, is of length j' k And then X k Is a fixed n x j' k Matrix of (h), h j4 Is the interpretation variable gamma jk J=1, 2,3 …, J k K=1, 2,3,4. Since the GAMLSS model has a high degree of freedom, each parameter of its hypothetical distribution of response variables can be fitted by an additive function of interpretation variables, and is therefore suitable for model fitting for bias or various distribution types. This study was analyzed using the "gamls" package (GAMLSS method) in R software (Version 3.3.3, http:// www.r-project. Org). After modeling, screening was performed based on the AIC (Akaike Information Criterion) and BIC (Bayesian information criterion) min principles. AIC is a system ofThe principle of the red Chi Hong of the students is mainly based on the information theory. When the statistical model is fit to the data, errors can always exist; thus, using a model to represent a fit may lose some information. AIC estimates the relative amount of information lost by the fitted model: the less information the model loses, the higher the quality of the model. AIC is generally expressed as:
AIC＝2k-2ln(L) (2-6)
k is expressed as the number of independent parameters of the model, and L is expressed as a maximum likelihood function of the model. BIC is a model selection criterion proposed by Schwarz in 1978, and is generally expressed as:
BIC＝ln(n)*k-2ln(L) (2-7)
where k is the number of model parameters, n is the sample size, and L is the likelihood function. In order to avoid dimension disasters, guarantees of ln (n) ×k still apply the screening criteria in the case of larger dimension and smaller sample data size. For this purpose we also fit a linear model, a cubic curve model and a GAMLSS model, comparing the sizes of AIC and BIC in the different models to evaluate the fit of the models.
And fitting a gestational pregnancy and estrogen change level reference model by using a GAMLSS model based on normal gestational and estrogen levels of the puerperal.
Table 2-1 shows that the change in the level of progestogen does not appear linear but rises curvilinearly with increasing gestational age, and that the concentration changes even more than 1000-fold with increasing gestational age. Thus, gestational age will be included in the spline model as the primary argument. Furthermore, there is evidence for research that hormone levels in females change with age, and thus pregnant women's age is also a model modification variable. The models were screened using the GAMLSS model according to AIC and BIC minimization principles, and the regression coefficients of the final models are shown in tables 2-1 to 2-3.
TABLE 2-1 establishment of the coefficients of the progesterone prediction model during pregnancy based on GAMLSS
TABLE 2-2 coefficients for establishing a pregnancy estradiol predictive model based on GAMLSS
TABLE 2-3 coefficients for establishing a pregnancy estriol prediction model based on GAMLSS
Firstly, we perform fitting effect longitudinal evaluation on the fitted GAMLSS, and figures 2-3 are residual scattered point distribution diagrams and residual Q-Q diagrams after fitting each hormone optimal model. The normalized residuals are substantially distributed over the predicted values, and are substantially-5<Z-score <5. The residual error distribution is proved to be uniform, and the model fitting effect is good.
Tables 2-4 Linear, cubic and GAMLSS model fitting of AIC and BIC comparisons of pregnancy and estrogens
Furthermore, we performed the same fitting using the linear model and the cubic model [27] proposed in the other studies, and tables 2-4 show information content comparisons of AIC and BIC after the linear model, the cubic model, and the GAMLSS model fitting. The results show that the AIC and BIC information content of the GAMLSS is minimal compared to conventional linear and cubic curve fits, suggesting that the GAMLSS has better effect of the fit on pregnancy, estrogen levels than conventional models.
Figures 2-5 show the predicted value of estrogen and progestin increase fitted by the GAMLSS method, respectively. It can be seen from the predicted curve that progesterone showed an accelerated increase 7 weeks before pregnancy, followed by a gradual increase and a rapid increase after 35 weeks. Estradiol shows an accelerated increase during pregnancy until it shows a slow decrease after 30 weeks. Estriol exhibits an accelerated growth phase at 28-35 weeks, followed by a wave motion.
In order to further explore the connection between premature birth and pregnancy and estrogen, the gestational age and age of 520 premature pregnant women are included in the predictive model according to the predictive model of pregnancy and estrogen change, so as to obtain hormone predictive values, and the hormone predictive values are compared with actual detection values. Table 2-2 shows a comparison of the detected value of oestrogen and progestin and the predicted value of GAMLSS for a pregnant woman with premature pregnancy. The actual detected progestin was lower for preterm pregnant women than for the reference values predicted by GAMLSS, and the differences were statistically significant (P < 0.05). In contrast, the hormone concentrations of estradiol and estriol are higher. Suggesting that premature delivery may be associated with low progestin and high estrogen levels.
Table 2-5 comparison of the differences between the detection values of the estrogens and the predicted GAMLSS values of premature pregnant women
The above is merely a further embodiment of the present invention, but the protection scope of the present invention is not limited thereto, and any person skilled in the art will be able to apply equivalents and modifications according to the technical solution and the concept of the present invention within the scope of the present invention disclosed in the present invention.
Claims (9)
1. A semi-parametric model-based GAMLSS method for predicting pregnancy females and progestins, comprising the steps of:
step one: recruiting clinical labor examination pregnant women;
step two: detecting the level of pregnancy and estrogen in the serum of the pregnant woman by using a full-automatic chemiluminescence immunoassay analyzer and a chemiluminescence method;
step three: according to the detection value data structure, sequentially setting the average value coefficient mu parameter as identity and log connection;
step four: the adopted specific detection value data structure model distribution is respectively set as Box-Cox normal distribution, box-Cox-t distribution, generalized Beta type 1 distribution, gamma distribution and power exponent distribution, and an optimal model is determined according to AIC and BIC minimum values.
2. The method for predicting pregnancy female and progestin based on semi-parametric model GAMLSS according to claim 1, wherein: specific continuity data for progestins and estrogens are measured;
in particular, the patient a estradiol: 20.81ng/mL progesterone: 30.23ng/mL;
patient b estradiol: 30.15ng/mL progesterone: 30.78ng/mL;
belonging to the bias distribution.
3. A method for predicting pregnancy estrogens and progestins based on a semi-parametric model GAMLSS according to claim 2, wherein: the basic probability density function using GAMLSS is f (y i |θ i )；
Wherein:
θ i ＝(μ i ，σ i ，γ i ，τ i )；
μ i as a position parameter, representing a distribution mean;
σ i the scale parameter represents standard deviation;
γ i representing the skewness of the distribution;
τ i is the kurtosis of the distribution.
4. A method for predicting pregnancy estrogens and progestins based on a semi-parametric model GAMLSS according to claim 3, wherein: the fitting form of the GAMLSS function is:
thus, the fitting of four parameters is of general form:
5. the method for predicting pregnancy female and progestin based on semi-parametric model GAMLSS according to claim 4, wherein: μ, θ, γ, τ are vectors of length n;
is of length j' k Is a vector of (2);
X k is a fixed n x j' k Is a matrix of (a);
h j4 is the interpretation variable gamma jk Is a smooth non-parametric function of (c);
j＝1,2,3…,J k ，k＝1,2,3,4。
6. the method for predicting pregnancy female and progestin based on semi-parametric model GAMLSS according to claim 5, wherein: analysis was performed using the GAMLSS method, and after modeling, screening was performed based on the Akaike Information Criterion and Bayesian information criterion minimums.
7. The method for predicting pregnancy female and progestin based on semi-parametric model GAMLSS according to claim 6, wherein: errors can exist when the statistical model fits to the data;
using a model to represent a fit may lose some information.
8. The method for predicting pregnancy female and progestin based on semi-parametric model GAMLSS according to claim 7, wherein: akaike Information Criterion is expressed as: akaike Information Criterion =2k—2ln (L);
k is expressed as the number of independent parameters of the model, and L is expressed as a maximum likelihood function of the model.
9. The method for predicting pregnancy female and progestin based on semi-parametric model GAMLSS according to claim 8, wherein: bayesian information criterion is expressed as: bayesian information criterion =ln (n) ×k-2ln (L);
where k is the number of model parameters, n is the sample size, and L is the likelihood function.
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